# A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions

arXiv: https://arxiv.org/abs/2205.01094 · HTML: https://mlsanity.com/paper/2205.01094/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 54 references that are in the map.
- First posted on arXiv 1 May 2022.

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment](https://mlsanity.com/paper/1907.11932/) (2019)
- [Adversarial Examples for Evaluating Reading Comprehension Systems](https://mlsanity.com/paper/1707.07328/) (2017)
- [BERT-ATTACK: Adversarial Attack Against BERT Using BERT](https://mlsanity.com/paper/2004.09984/) (2020)
- [Generating Natural Language Adversarial Examples](https://mlsanity.com/paper/1804.07998/) (2018)
- [BAE: BERT-based Adversarial Examples for Text Classification](https://mlsanity.com/paper/2004.01970/) (2020)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [Adversarial Example Generation with Syntactically Controlled Paraphrase Networks](https://mlsanity.com/paper/1804.06059/) (2018)
- [Gradient-based Adversarial Attacks against Text Transformers](https://mlsanity.com/paper/2104.13733/) (2021)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Temporal Relational Ranking for Stock Prediction](https://mlsanity.com/paper/1809.09441/) (2018)
- [Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI](https://mlsanity.com/paper/1909.02309/) (2019)
- [Towards Robustness Against Natural Language Word Substitutions](https://mlsanity.com/paper/2107.13541/) (2021)
- [Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!](https://mlsanity.com/paper/2103.10013/) (2021)
- [Universal Adversarial Attacks with Natural Triggers for Text Classification](https://mlsanity.com/paper/2005.00174/) (2020)
- [Generating Fluent Adversarial Examples for Natural Languages](https://mlsanity.com/paper/2007.06174/) (2020)
- [Sentiment Analysis Based on Deep Learning: A Comparative Study](https://mlsanity.com/paper/2006.03541/) (2020)
- [Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification](https://mlsanity.com/paper/1812.00151/) (2018)
- [Grey-box Adversarial Attack And Defence For Sentiment Classification](https://mlsanity.com/paper/2103.11576/) (2021)
- [RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models](https://mlsanity.com/paper/2109.05620/) (2021)
- [Generating Adversarial Computer Programs using Optimized Obfuscations](https://mlsanity.com/paper/2103.11882/) (2021)
- [Multi-granularity Textual Adversarial Attack with Behavior Cloning](https://mlsanity.com/paper/2109.04367/) (2021)
- [On the Transferability of Adversarial Attacksagainst Neural Text Classifier](https://mlsanity.com/paper/2011.08558/) (2020)
- [A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks](https://mlsanity.com/paper/2011.10492/) (2020)

## Built on by (most cited first, 15 of 15)

- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023)
- [Text-CRS: A Generalized Certified Robustness Framework against Textual Adversarial Attacks](https://mlsanity.com/paper/2307.16630/) (2023)
- [Efficient Continual Pre-training for Building Domain Specific Large Language Models](https://mlsanity.com/paper/2311.08545/) (2023)
- [ChatGPT as an Attack Tool: Stealthy Textual Backdoor Attack via Blackbox Generative Model Trigger](https://mlsanity.com/paper/2304.14475/) (2023)
- [Does More Advice Help? The Effects of Second Opinions in AI-Assisted Decision Making](https://mlsanity.com/paper/2401.07058/) (2024)
- [Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms](https://mlsanity.com/paper/2207.09572/) (2022)
- [CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability](https://mlsanity.com/paper/2310.14265/) (2023)
- [AMA-LSTM: Pioneering Robust and Fair Financial Audio Analysis for Stock Volatility Prediction](https://mlsanity.com/paper/2407.18324/) (2024)
- [Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms](https://mlsanity.com/paper/2412.18202/) (2024)
- [Empirical Asset Pricing with Large Language Model Agents](https://mlsanity.com/paper/2409.17266/) (2024)
- [NLP Security and Ethics, in the Wild](https://mlsanity.com/paper/2504.06669/) (2025)
- [Style Attack Disguise: When Fonts Become a Camouflage for Adversarial Intent](https://mlsanity.com/paper/2510.19641/) (2025)
- [FinTech for Social Good: A Research Agenda from NLP Perspective](https://mlsanity.com/paper/2211.06431/) (2022)
- [Guillotine: Hypervisors for Isolating Malicious AIs](https://mlsanity.com/paper/2504.15499/) (2025)
- [GADFA: Generator-Assisted Decision-Focused Approach for Opinion Expressing Timing Identification](https://mlsanity.com/paper/2410.01169/) (2024)

## Abstract (arXiv)

More and more investors and machine learning models rely on social media (e.g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements. Although text-based models are known to be vulnerable to adversarial attacks, whether stock prediction models have similar vulnerability is underexplored. In this paper, we experiment with a variety of adversarial attack configurations to fool three stock prediction victim models. We address the task of adversarial generation by solving combinatorial optimization problems with semantics and budget constraints. Our results show that the proposed attack method can achieve consistent success rates and cause significant monetary loss in trading simulation by simply concatenating a perturbed but semantically similar tweet.